Spatial Interpolation of Cyclostationary Test Statistics in Cognitive Radio Networks: Methods and Field Measurements

Abstract

<p>The focus of this paper is on evaluating different spatial interpolation methods for the construction of radio environment map (REM) using field measurements obtained by cyclostationary based mobile sensors. Mobile sensing devices employing cyclostationary detectors provide lot of advantages compared to widely used energy detectors such as robustness to noise uncertainty and ability to distinguish among different primary user signals. However, mobile sensing results are not available at locations between the sensors making it difficult for a secondary user (possibly without a spectrum sensor) to decide whether or not to use primary user resources at that location. To overcome this, spatial interpolation of test statistics measured at limited number of locations can be carried out to create a channel occupancy map at unmeasured locations between the sensors. For this purpose, different spatial interpolation techniques for the cyclostationary test statistic have been employed in this paper such as inverse distance weighting (IDW), ordinary Kriging (OK), and universal Kriging (UK). The effectiveness of these methods is demonstrated by applying them on extensive real-world field measurement data obtained by mobile-phone-compliant spectrum sensors. The field measurements were carried out using four mobile spectrum sensors measuring eight DVB-T channels at more than hundred locations encompassing roughly one-third of the area of the city of Espoo in Finland. The accuracy of the spatial interpolation results based on the field measurements is determined using the cross validation approach with the widely used root mean square error (RMSE) as the metric. Field measurement results indicate that reliable results with spatial coverage can be achieved using Kriging for cyclostationary based test statistics. Comparison of spatial interpolation results of cyclostationary test statistics is also carried out with those of energy values obtained during the measurement campaign in the form of received signal strength indicator (RSSI). Comparison results clearly show the performance improvement and robustness obtained by the use of cyclostationary based detectors instead of energy detectors.</p>